Content based selection and meta tagging of advertisement breaks

ABSTRACT

An example system to identify an advertisement to include in source material to increase an effectiveness of the advertisement includes an analyzer to determine one or more priming characteristics for a plurality of locations of a source material based on neuro-response data collected from a first subject exposed to the source material and a selector to identify an attribute of the advertisement, identify at least one of a temporal attribute or a spatial attribute for the plurality of locations, perform a comparison of the attribute of the advertisement to the at least one of the temporal attribute or the spatial attribute for the plurality of locations, select a first location of the plurality of locations for insertion of the advertisement based on the comparison and the priming characteristics, and transform the source material to include the advertisement at the first location.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 13/730,564, which was filed on Dec. 28, 2012, and which is acontinuation of U.S. patent application Ser. No. 12/200,813, which wasfiled on Aug. 28, 2008, and claims the benefit under 35 U.S.C. § 119(e)of U.S. Patent Provisional Application 60/968,567, which was filed onAug. 29, 2007. U.S. patent application Ser. No. 13/730,564, U.S. patentapplication Ser. No. 12/200,813, and U.S. Provisional Patent Application60/968,567 are hereby incorporated by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates to content based selection andmeta-tagging of advertisement breaks.

BACKGROUND

Conventional systems for content selection and meta-tagging ofadvertisement breaks are limited or non-existent. Some conventionalsystems provide very rudimentary information for content selectionthrough demographic information and statistical data. However,conventional systems are subject to semantic, syntactic, metaphorical,cultural, and interpretive errors.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular examples.

FIG. 1 illustrates one example of a system for content selection andmeta-tagging of advertisement breaks.

FIG. 2 illustrates examples of stimulus attributes that can be includedin a stimulus attributes repository.

FIG. 3 illustrates examples of data models that can be used with astimulus and response repository.

FIG. 4 illustrates one example of a query that can be used with thecontent selection and meta-tagging system.

FIG. 5 illustrates one example of a report generated using the contentselection and meta-tagging system.

FIG. 6 illustrates one example of a technique for performing dataanalysis.

FIG. 7 illustrates one example of technique for content selection andmeta-tagging of advertisement breaks.

FIG. 8 provides one example of a system that can be used to implementone or more mechanisms.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of thedisclosure including the best modes contemplated by the inventors forcarrying out the teachings of the disclosure. Examples of these specificexamples are illustrated in the accompanying drawings. While thedisclosure is described in conjunction with these specific examples, itwill be understood that it is not intended to limit the disclosure tothe described examples. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the disclosure as defined by the appendedclaims.

For example, the techniques and mechanisms of the present disclosurewill be described in the context of particular types of data such ascentral nervous system, autonomic nervous system, and effector data.However, it should be noted that the techniques and mechanisms of thepresent disclosure apply to a variety of different types of data. Itshould be noted that various mechanisms and techniques can be applied toany type of stimuli. In the following description, numerous specificdetails are set forth in order to provide a thorough understanding ofthe present disclosure. Particular examples of the present disclosuremay be implemented without some or all of these specific details. Inother instances, well known process operations have not been describedin detail in order not to unnecessarily obscure the present disclosure.

Various techniques and mechanisms of the present disclosure willsometimes be described in singular form for clarity. However, it shouldbe noted that some examples include multiple iterations of a techniqueor multiple instantiations of a mechanism unless noted otherwise. Forexample, a system uses a processor in a variety of contexts. However, itwill be appreciated that a system can use multiple processors whileremaining within the scope of the present disclosure unless otherwisenoted. Furthermore, the techniques and mechanisms of the presentdisclosure will sometimes describe a connection between two entities. Itshould be noted that a connection between two entities does notnecessarily mean a direct, unimpeded connection, as a variety of otherentities may reside between the two entities. For example, a processormay be connected to memory, but it will be appreciated that a variety ofbridges and controllers may reside between the processor and memory.Consequently, a connection does not necessarily mean a direct, unimpededconnection unless otherwise noted.

Overview

Consequently, it is desirable to provide improved methods and apparatusfor content selection and meta-tagging of advertisement (ad) breaks.

A system evaluates stimulus materials such as videos, imagery, webpages, text, etc., in order to determine resonance and priming levelsfor various products and services at different temporal and spatiallocations including advertisement breaks in the stimulus materials. Thestimulus materials are tagged with resonance and priming levelinformation to allow intelligent selection of suitable advertisementcontent for insertion at various locations in the stimulus materials.Response data such as survey data and/or neuro-response data includingEvent Related Potential (ERP), Electroencephalography (EEG), GalvanicSkin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG),eye tracking, and facial emotion encoding data may be used to determineresonance and priming levels.

Examples

Conventional mechanisms for selecting advertising content rely ondemographic information, statistical information, and survey basedresponse collection. One problem with conventional mechanisms forselecting advertising is that they do not measure the inherent messageresonance and priming for various products, services, and offerings thatare attributable to the stimulus. They are also prone to semantic,syntactic, metaphorical, cultural, and interpretive errors therebypreventing the accurate and repeatable targeting of the audience.

Conventional systems do not use neuro-behavioral and neuro-physiologicalresponse blended manifestations in assessing the user response and donot elicit an individual customized neuro-physiological and/orneuro-behavioral response to the stimulus. Conventional systems alsofail to blend multiple datasets, and blended manifestations ofmulti-modal responses, across multiple datasets, individuals andmodalities, to reveal and validate the elicited measures of resonanceand priming to allow for intelligent selection of advertising content.

In these respects, the content selection and meta-tagging of advertisingbreak system according to the present disclosure substantially departsfrom the conventional concepts and designs of the prior art. Accordingto various examples, it is recognized that advertisements for particularproducts, services, and offerings may be particularly effective when asubject is primed for the particular products, services, and offerings.For examples, an advertisement for cleaning supplies may be particularlyeffective after viewers watch a scene showing a dirty room, or anadvertisement for a fuel efficient car may be particularly effectiveafter viewers watch a documentary about high oil prices. In still otherexamples, an audio advertisement for packaged salads may be moreeffective after viewers hear a radio program about coronary disease, ora brand image for camping products may be more effective placed near amural showing mountain scenery.

Consequently, the techniques and mechanisms of the present disclosuretag stimulus materials such as video, audio, web pages, printedmaterials, etc. with information indicating resonance and/or priminglevels for various products, services and offerings. Meta-tags may bestored in a separate repository or in the stimulus material itself. Insome examples, advertising content suitable for particular priminglevels may be automatically selected based on meta-tags for introductioninto the stimulus materials. In other examples, advertising content canbe intelligently inserted based on priming levels for particularproducts and services. In some examples, advertising break slots can besold or auctioned more efficiently based on priming levels andresonance. Advertisers can assess the value of particular slots based onpriming levels and resonance.

According to various examples, the techniques and mechanisms of thepresent disclosure may use a variety of mechanisms such as survey basedresponses, statistical data, and/or neuro-response measurements such ascentral nervous system, autonomic nervous system, and effectormeasurements to improve content selection and meta-tagging of stimulusmaterial. Some examples of central nervous system measurement mechanismsinclude Functional Magnetic Resonance Imaging (fMRI) andElectroencephalography (EEG). fMRI measures blood oxygenation in thebrain that correlates with increased neural activity. However, currentimplementations of FMRI have poor temporal resolution of few seconds.EEG measures electrical activity associated with post synaptic currentsoccurring in the milliseconds range. Subcranial EEG can measureelectrical activity with the most accuracy, as the bone and dermallayers weaken transmission of a wide range of frequencies. Nonetheless,surface EEG provides a wealth of electrophysiological information ifanalyzed properly. Even portable EEG with dry electrodes provides alarge amount of neuro-response information.

Autonomic nervous system measurement mechanisms include Galvanic SkinResponse (GSR), Electrocardiograms (EKG), pupillary dilation, etc.Effector measurement mechanisms include Electrooculography (EOG), eyetracking, facial emotion encoding, reaction time etc.

According to various examples, the techniques and mechanisms of thepresent disclosure intelligently blend multiple modes and manifestationsof precognitive neural signatures with cognitive neural signatures andpost cognitive neurophysiological manifestations to more accuratelyperform content selection and meta-tagging. In some examples, autonomicnervous system measures are themselves used to validate central nervoussystem measures. Effector and behavior responses are blended andcombined with other measures. According to various examples, centralnervous system, autonomic nervous system, and effector systemmeasurements are aggregated into a measurement that allows contentselection and meta-tagging of advertising breaks.

In particular examples, subjects are exposed to stimulus material anddata such as central nervous system, autonomic nervous system, andeffector data is collected during exposure. According to variousexamples, data is collected in order to determine a resonance measurethat aggregates multiple component measures that assess resonance data.In particular examples, specific event related potential (ERP) analysesand/or event related power spectral perturbations (ERPSPs) are evaluatedfor different regions of the brain both before a subject is exposed tostimulus and each time after the subject is exposed to stimulus.

Pre-stimulus and post-stimulus differential as well as target anddistracter differential measurements of ERP time domain components atmultiple regions of the brain are determined (DERP). Event relatedtime-frequency analysis of the differential response to assess theattention, emotion and memory retention (DERPSPs) across multiplefrequency bands including but not limited to theta, alpha, beta, gammaand high gamma is performed. In particular examples, single trial and/oraveraged DERP and/or DERPSPs can be used to enhance the resonancemeasure and determine priming levels for various products and services.

A variety of stimulus materials such as entertainment and marketingmaterials, media streams, billboards, print advertisements, textstreams, music, performances, sensory experiences, etc. can be analyzed.According to various examples, enhanced neuro-response data is generatedusing a data analyzer that performs both intra-modality measurementenhancements and cross-modality measurement enhancements. According tovarious examples, brain activity is measured not just to determine theregions of activity, but to determine interactions and types ofinteractions between various regions. The techniques and mechanisms ofthe present disclosure recognize that interactions between neuralregions support orchestrated and organized behavior. Attention, emotion,memory, and other abilities are not merely based on one part of thebrain but instead rely on network interactions between brain regions.

The techniques and mechanisms of the present disclosure furtherrecognize that different frequency bands used for multi-regionalcommunication can be indicative of the effectiveness of stimuli. Inparticular examples, evaluations are calibrated to each subject andsynchronized across subjects. In particular examples, templates arecreated for subjects to create a baseline for measuring pre and poststimulus differentials. According to various examples, stimulusgenerators are intelligent and adaptively modify specific parameterssuch as exposure length and duration for each subject being analyzed.

A variety of modalities can be used including EEG, GSR, EKG, pupillarydilation, EOG, eye tracking, facial emotion encoding, reaction time,etc. Individual modalities such as EEG are enhanced by intelligentlyrecognizing neural region communication pathways. Cross modalityanalysis is enhanced using a synthesis and analytical blending ofcentral nervous system, autonomic nervous system, and effectorsignatures. Synthesis and analysis by mechanisms such as time and phaseshifting, correlating, and validating intra-modal determinations allowgeneration of a composite output characterizing the significance ofvarious data responses to effectively perform content selection andmeta-tagging.

FIG. 1 illustrates one example of a system for performing contentselection and meta-tagging using central nervous system, autonomicnervous system, and/or effector measures. According to various examples,the content selection and meta-tagging system includes a stimuluspresentation device 101. In particular examples, the stimuluspresentation device 101 is merely a display, monitor, screen, etc., thatdisplays stimulus material to a user. The stimulus material may be amedia clip, a commercial, pages of text, a brand image, a performance, amagazine advertisement, a movie, an audio presentation, and may eveninvolve particular tastes, smells, textures and/or sounds. The stimulican involve a variety of senses and occur with or without humansupervision. Continuous and discrete modes are supported. According tovarious examples, the stimulus presentation device 101 also has protocolgeneration capability to allow intelligent customization of stimuliprovided to multiple subjects in different markets.

According to various examples, stimulus presentation device 101 couldinclude devices such as televisions, cable consoles, computers andmonitors, projection systems, display devices, speakers, tactilesurfaces, etc., for presenting the stimuli including but not limited toadvertising and entertainment from different networks, local networks,cable channels, syndicated sources, websites, internet contentaggregators, portals, service providers, etc.

According to various examples, the subjects are connected to datacollection devices 105. The data collection devices 105 may include avariety of neuro-response measurement mechanisms including neurologicaland neurophysiological measurements systems such as EEG, EOG, GSR, EKG,pupillary dilation, eye tracking, facial emotion encoding, and reactiontime devices, etc. According to various examples, neuro-response dataincludes central nervous system, autonomic nervous system, and effectordata. In particular examples, the data collection devices 105 includeEEG 111, EOG 113, and GSR 115. In some instances, only a single datacollection device is used. Data collection may proceed with or withouthuman supervision.

The data collection device 105 collects neuro-response data frommultiple sources. This includes a combination of devices such as centralnervous system sources (EEG), autonomic nervous system sources (GSR,EKG, pupillary dilation), and effector sources (EOG, eye tracking,facial emotion encoding, reaction time). In particular examples, datacollected is digitally sampled and stored for later analysis. Inparticular examples, the data collected could be analyzed in real-time.According to particular examples, the digital sampling rates areadaptively chosen based on the neurophysiological and neurological databeing measured.

In one particular example, the content selection and meta-tagging systemincludes EEG 111 measurements made using scalp level electrodes, EOG 113measurements made using shielded electrodes to track eye data, GSR 115measurements performed using a differential measurement system, a facialmuscular measurement through shielded electrodes placed at specificlocations on the face, and a facial affect graphic and video analyzeradaptively derived for each individual.

In particular examples, the data collection devices are clocksynchronized with a stimulus presentation device 101. In particularexamples, the data collection devices 105 also include a conditionevaluation subsystem that provides auto triggers, alerts and statusmonitoring and visualization components that continuously monitor thestatus of the subject, data being collected, and the data collectioninstruments. The condition evaluation subsystem may also present visualalerts and automatically trigger remedial actions. According to variousexamples, the data collection devices include mechanisms for not onlymonitoring subject neuro-response to stimulus materials, but alsoinclude mechanisms for identifying and monitoring the stimulusmaterials. For example, data collection devices 105 may be synchronizedwith a set-top box to monitor channel changes. In other examples, datacollection devices 105 may be directionally synchronized to monitor whena subject is no longer paying attention to stimulus material. In stillother examples, the data collection devices 105 may receive and storestimulus material generally being viewed by the subject, whether thestimulus is a program, a commercial, printed material, or a sceneoutside a window. The data collected allows analysis of neuro-responseinformation and correlation of the information to actual stimulusmaterial and not mere subject distractions.

According to various examples, the content selection and meta-taggingsystem also includes a data cleanser device 121. In particular examples,the data cleanser device 121 filters the collected data to remove noise,artifacts, and other irrelevant data using fixed and adaptive filtering,weighted averaging, advanced component extraction (like PCA, ICA),vector and component separation methods, etc. This device cleanses thedata by removing both exogenous noise (where the source is outside thephysiology of the subject, e.g. a phone ringing while a subject isviewing a video) and endogenous artifacts (where the source could beneurophysiological, e.g. muscle movements, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

According to various examples, the data cleanser device 121 isimplemented using hardware, firmware, and/or software. It should benoted that although a data cleanser device 121 is shown located after adata collection device 105 and before data analyzer 181, the datacleanser device 121 like other components may have a location andfunctionality that varies based on system implementation. For example,some systems may not use any automated data cleanser device whatsoeverwhile in other systems, data cleanser devices may be integrated intoindividual data collection devices.

According to various examples, an optional data meta attributesrepository 131 provides information on the stimulus material beingpresented. According to various examples, stimulus attributes includeproperties of the stimulus materials as well as purposes, presentationattributes, report generation attributes, etc. In particular examples,stimulus attributes include time span, channel, rating, media, type,etc. Stimulus attributes may also include positions of entities invarious frames, object relationships, locations of objects and durationof display. Purpose attributes include aspiration and objects of thestimulus including excitement, memory retention, associations, etc.Presentation attributes include audio, video, imagery, and messagesneeded for enhancement or avoidance. Other attributes may or may notalso be included in the stimulus attributes repository or some otherrepository.

The data cleanser device 121 and the stimulus attributes repository 131pass data to the data analyzer 181. The data analyzer 181 uses a varietyof mechanisms to analyze underlying data in the system to determineresonance. According to various examples, the data analyzer customizesand extracts the independent neurological and neuro-physiologicalparameters for each individual in each modality, and blends theestimates within a modality as well as across modalities to elicit anenhanced response to the presented stimulus material. In particularexamples, the data analyzer 181 aggregates the response measures acrosssubjects in a dataset.

According to various examples, neurological and neuro-physiologicalsignatures are measured using time domain analyses and frequency domainanalyses. Such analyses use parameters that are common acrossindividuals as well as parameters that are unique to each individual.The analyses could also include statistical parameter extraction andfuzzy logic based attribute estimation from both the time and frequencycomponents of the synthesized response.

In some examples, statistical parameters used in a blended effectivenessestimate include evaluations of skew, peaks, first and second moments,population distribution, as well as fuzzy estimates of attention,emotional engagement and memory retention responses.

According to various examples, the data analyzer 181 may include anintra-modality response synthesizer and a cross-modality responsesynthesizer. In particular examples, the intra-modality responsesynthesizer is configured to customize and extract the independentneurological and neurophysiological parameters for each individual ineach modality and blend the estimates within a modality analytically toelicit an enhanced response to the presented stimuli. In particularexamples, the intra-modality response synthesizer also aggregates datafrom different subjects in a dataset.

According to various examples, the cross-modality response synthesizeror fusion device blends different intra-modality responses, includingraw signals and signals output. The combination of signals enhances themeasures of effectiveness within a modality. The cross-modality responsefusion device can also aggregate data from different subjects in adataset.

According to various examples, the data analyzer 181 also includes acomposite enhanced effectiveness estimator (CEEE) that combines theenhanced responses and estimates from each modality to provide a blendedestimate of the effectiveness. In particular examples, blended estimatesare provided for each exposure of a subject to stimulus materials. Theblended estimates are evaluated over time to assess resonancecharacteristics. According to various examples, numerical values areassigned to each blended estimate. The numerical values may correspondto the intensity of neuro-response measurements, the significance ofpeaks, the change between peaks, etc. Higher numerical values maycorrespond to higher significance in neuro-response intensity. Lowernumerical values may correspond to lower significance or eveninsignificant neuro-response activity. In other examples, multiplevalues are assigned to each blended estimate. In still other examples,blended estimates of neuro-response significance are graphicallyrepresented to show changes after repeated exposure.

According to various examples, the data analyzer 181 provides analyzedand enhanced response data to a data communication device. It should benoted that in particular instances, a data communication device is notnecessary. According to various examples, the data communication deviceprovides raw and/or analyzed data and insights. In particular examples,the data communication device may include mechanisms for the compressionand encryption of data for secure storage and communication.

According to various examples, the data communication device transmitsdata using protocols such as the File Transfer Protocol (FTP), HypertextTransfer Protocol (HTTP) along with a variety of conventional, bus,wired network, wireless network, satellite, and proprietarycommunication protocols. The data transmitted can include the data inits entirety, excerpts of data, converted data, and/or elicited responsemeasures. According to various examples, the data communication deviceis a set top box, wireless device, computer system, etc. that transmitsdata obtained from a data collection device to a resonance estimator185. In particular examples, the data communication device may transmitdata even before data cleansing or data analysis. In other examples, thedata communication device may transmit data after data cleansing andanalysis.

In particular examples, the data communication device sends data to aresonance estimator 185. According to various examples, the resonanceestimator 185 assesses and extracts resonance patterns. In particularexamples, the resonance estimator 185 determines entity positions invarious stimulus segments and matches position information with eyetracking paths while correlating saccades with neural assessments ofattention, memory retention, and emotional engagement. In particularexamples, the resonance estimator 185 also collects and integrates userbehavioral and survey responses with the analyzed response data to moreeffectively estimate resonance.

According to various examples, the resonance estimator 185 provides datato a priming repository system 187. In particular examples, the primingrepository system 187 associates meta-tags with various temporal andspatial locations in stimulus material, such as a television program,movie, video, audio program, print advertisement, etc. In some examples,every second of a show is associated with a set of meta-tags. In otherexamples, commercial or advertisement (ad) breaks are provided with aset of meta-tags that identify commercial or advertising content thatwould be most suitable for a particular break.

Pre-break content may identify categories of products and services thatare primed at a particular point in a program. The content may alsospecify the level of priming associated with each category of product orservice. For example, a movie may show old house and buildings.Meta-tags may be manually or automatically generated to indicate thatcommercials for home improvement products would be suitable for aparticular advertisement break.

In some instances, meta-tags may include spatial and temporalinformation indicating where and when particular advertisements shouldbe placed. For example, a documentary about wildlife that shows a blankwall in several scenes may include meta-tags that indicate a banneradvertisement for nature oriented vacations may be suitable. Theadvertisements may be separate from a program or integrated into aprogram. According to various examples, the priming repository system187 also identifies scenes eliciting significant audience resonance toparticular products and services as well as the level and intensity ofresonance.

A variety of data can be stored for later analysis, management,manipulation, and retrieval. In particular examples, the repositorycould be used for tracking stimulus attributes and presentationattributes, audience responses optionally could also be integrated intometa-tags.

As with a variety of the components in the system, the repository can beco-located with the rest of the system and the user, or could beimplemented in a remote location. It could also be optionally separatedinto repository system that could be centralized or distributed at theprovider or providers of the stimulus material. In other examples, therepository system itself is integrated into a library of stimulusmaterials such as a media library.

FIG. 2 illustrates examples of data models that may be provided with astimulus attributes repository. According to various examples, astimulus attributes data model 201 includes a channel 203, media type205, time span 207, audience 209, and demographic information 211. Astimulus purpose data model 215 may include intents 217 and objectives219. According to various examples, stimulus attributes data model 201also includes spatial and temporal information 221 about entities andemerging relationships between entities.

According to various examples, another stimulus attributes data model221 includes creation attributes 223, ownership attributes 225,broadcast attributes 227, and statistical, demographic and/or surveybased identifiers 229 for automatically integrating theneuro-physiological and neuro-behavioral response with other attributesand meta-information associated with the stimulus.

According to various examples, a stimulus priming data model 231includes fields for identifying advertisement breaks 233 and scenes 235that can be associated with various priming levels 237 and audienceresonance measurements 239. In particular examples, the data model 231provides temporal and spatial information for ads, scenes, events,locations, etc. that may be associated with priming levels and audienceresonance measurements. In some examples, priming levels for a varietyof products, services, offerings, etc. are correlated with temporal andspatial information in source material such as a movie, billboard,advertisement, commercial, store shelf, etc. In some examples, the datamodel associates with each second of a show a set of meta-tags forpre-break content indicating categories of products and services thatare primed. The level of priming associated with each category ofproduct or service when the advertisement break is specified may also beprovided. Audience resonance measurements and maximal audience resonancemeasurements for various scenes and advertisement breaks may bemaintained and correlated with sets of products, services, offerings,etc.

The priming and resonance information may be used to select stimuluscontent suited for particular levels of priming and resonance. In someexamples, the priming and resonance information may be used to moreintelligently price advertising breaks based on value to advertisers.

FIG. 3 illustrates examples of data models that can be used for storageof information associated with tracking and measurement of resonance.According to various examples, a dataset data model 301 includes anexperiment name 303 and/or identifier, client attributes 305, a subjectpool 307, logistics information 309 such as the location, date, and timeof testing, and stimulus material 311 including stimulus materialattributes.

In particular examples, a subject attribute data model 315 includes asubject name 317 and/or identifier, contact information 321, anddemographic attributes 319 that may be useful for review of neurologicaland neuro-physiological data. Some examples of pertinent demographicattributes include marriage status, employment status, occupation,household income, household size and composition, ethnicity, geographiclocation, sex, race. Other fields that may be included in data model 315include shopping preferences, entertainment preferences, and financialpreferences. Shopping preferences include favorite stores, shoppingfrequency, categories shopped, favorite brands. Entertainmentpreferences include network/cable/satellite access capabilities,favorite shows, favorite genres, and favorite actors. Financialpreferences include favorite insurance companies, preferred investmentpractices, banking preferences, and favorite online financialinstruments. A variety of subject attributes may be included in asubject attributes data model 315 and data models may be preset orcustom generated to suit particular purposes.

According to various examples, data models for neuro-feedbackassociation 325 identify experimental protocols 327, modalities included329 such as EEG, EOG, GSR, surveys conducted, and experiment designparameters 333 such as segments and segment attributes. Other fields mayinclude experiment presentation scripts, segment length, segment detailslike stimulus material used, inter-subject variations, intra-subjectvariations, instructions, presentation order, survey questions used,etc. Other data models may include a data collection data model 337.According to various examples, the data collection data model 337includes recording attributes 339 such as station and locationidentifiers, the data and time of recording, and operator details. Inparticular examples, equipment attributes 341 include an amplifieridentifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes likeEEG cap layout, active channels, sampling frequency, and filters used.EOG specific attributes include the number and type of sensors used,location of sensors applied, etc. Eye tracking specific attributesinclude the type of tracker used, data recording frequency, data beingrecorded, recording format, etc. According to various examples, datastorage attributes 345 include file storage conventions (format, namingconvention, dating convention), storage location, archival attributes,expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/oridentifier, an accessed data collection 353 such as data segmentsinvolved (models, databases/cubes, tables, etc.), access securityattributes 355 included who has what type of access, and refreshattributes 357 such as the expiry of the query, refresh frequency, etc.Other fields such as push-pull preferences can also be included toidentify an auto push reporting driver or a user driven report retrievalsystem.

FIG. 4 illustrates examples of queries that can be performed to obtaindata associated with content selection and meta-tagging. According tovarious examples, queries are defined from general or customizedscripting languages and constructs, visual mechanisms, a library ofpreset queries, diagnostic querying including drill-down diagnostics,and eliciting what if scenarios. According to various examples, subjectattributes queries 415 may be configured to obtain data from aneuro-informatics repository using a location 417 or geographicinformation, session information 421 such as testing times and dates,and demographic attributes 419. Demographics attributes includehousehold income, household size and status, education level, age ofkids, etc.

Other queries may retrieve stimulus material based on shoppingpreferences of subject participants, countenance, physiologicalassessment, completion status. For example, a user may query for dataassociated with product categories, products shopped, shops frequented,subject eye correction status, color blindness, subject state, signalstrength of measured responses, alpha frequency band ringers, musclemovement assessments, segments completed, etc. Experimental design basedqueries may obtain data from a neuro-informatics repository based onexperiment protocols 427, product category 429, surveys included 431,and stimulus provided 433. Other fields that may be used include thenumber of protocol repetitions used, combination of protocols used, andusage configuration of surveys.

Client and industry based queries may obtain data based on the types ofindustries included in testing, specific categories tested, clientcompanies involved, and brands being tested. Response assessment basedqueries 437 may include attention scores 439, emotion scores, 441,retention scores 443, and effectiveness scores 445. Such queries mayobtain materials that elicited particular scores.

Response measure profile based queries may use mean measure thresholds,variance measures, number of peaks detected, etc. Group response queriesmay include group statistics like mean, variance, kurtosis, p-value,etc., group size, and outlier assessment measures. Still other queriesmay involve testing attributes like test location, time period, testrepetition count, test station, and test operator fields. A variety oftypes and combinations of types of queries can be used to efficientlyextract data.

FIG. 5 illustrates examples of reports that can be generated. Accordingto various examples, client assessment summary reports 501 includeeffectiveness measures 503, component assessment measures 505, andresonance measures 507. Effectiveness assessment measures includecomposite assessment measure(s), industry/category/client specificplacement (percentile, ranking, etc.), actionable grouping assessmentsuch as removing material, modifying segments, or fine tuning specificelements, etc, and the evolution of the effectiveness profile over time.In particular examples, component assessment reports include componentassessment measures like attention, emotional engagement scores,percentile placement, ranking, etc. Component profile measures includetime based evolution of the component measures and profile statisticalassessments. According to various examples, reports include the numberof times material is assessed, attributes of the multiple presentationsused, evolution of the response assessment measures over the multiplepresentations, and usage recommendations.

According to various examples, client cumulative reports 511 includemedia grouped reporting 513 of all stimulus assessed, campaign groupedreporting 515 of stimulus assessed, and time/location grouped reporting517 of stimulus assessed. According to various examples, industrycumulative and syndicated reports 521 include aggregate assessmentresponses measures 523, top performer lists 525, bottom performer lists527, outliers 529, and trend reporting 531. In particular examples,tracking and reporting includes specific products, categories,companies, brands.

FIG. 6 illustrates one example of content selection and meta-tagging. At601, stimulus material is provided to multiple subjects. According tovarious examples, stimulus includes streaming video and audio. Inparticular examples, subjects view stimulus in their own homes in groupor individual settings. In some examples, verbal and written responsesare collected for use without neuro-response measurements. In otherexamples, verbal and written responses are correlated withneuro-response measurements. At 603, subject neuro-response measurementsare collected using a variety of modalities, such as EEG, ERP, EOG, GSR,etc. At 605, data is passed through a data cleanser to remove noise andartifacts that may make data more difficult to interpret. According tovarious examples, the data cleanser removes EEG electrical activityassociated with blinking and other endogenous/exogenous artifacts.

According to various examples, data analysis is performed. Data analysismay include intra-modality response synthesis and cross-modalityresponse synthesis to enhance effectiveness measures. It should be notedthat in some particular instances, one type of synthesis may beperformed without performing other types of synthesis. For example,cross-modality response synthesis may be performed with or withoutintra-modality synthesis.

A variety of mechanisms can be used to perform data analysis. Inparticular examples, a stimulus attributes repository 131 is accessed toobtain attributes and characteristics of the stimulus materials, alongwith purposes, intents, objectives, etc. In particular examples, EEGresponse data is synthesized to provide an enhanced assessment ofeffectiveness. According to various examples, EEG measures electricalactivity resulting from thousands of simultaneous neural processesassociated with different portions of the brain. EEG data can beclassified in various bands. According to various examples, brainwavefrequencies include delta, theta, alpha, beta, and gamma frequencyranges. Delta waves are classified as those less than 4 Hz and areprominent during deep sleep. Theta waves have frequencies between 3.5 to7.5 Hz and are associated with memories, attention, emotions, andsensations. Theta waves are typically prominent during states ofinternal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making. Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present disclosurerecognize that analyzing high gamma band (kappa-band: Above 60 Hz)measurements, in addition to theta, alpha, beta, and low gamma bandmeasurements, enhances neurological attention, emotional engagement andretention component estimates. In particular examples, EEG measurementsincluding difficult to detect high gamma or kappa band measurements areobtained, enhanced, and evaluated. Subject and task specific signaturesub-bands in the theta, alpha, beta, gamma and kappa bands areidentified to provide enhanced response estimates. According to variousexamples, high gamma waves (kappa-band) above 80 Hz (typicallydetectable with sub-cranial EEG and/or magnetoencephalograophy) can beused in inverse model-based enhancement of the frequency responses tothe stimuli.

Various examples of the present disclosure recognize that particularsub-bands within each frequency range have particular prominence duringcertain activities. A subset of the frequencies in a particular band isreferred to herein as a sub-band. For example, a sub-band may includethe 40-45 Hz range within the gamma band. In particular examples,multiple sub-bands within the different bands are selected whileremaining frequencies are band pass filtered. In particular examples,multiple sub-band responses may be enhanced, while the remainingfrequency responses may be attenuated.

An information theory based band-weighting model is used for adaptiveextraction of selective dataset specific, subject specific, taskspecific bands to enhance the effectiveness measure. Adaptive extractionmay be performed using fuzzy scaling. Stimuli can be presented andenhanced measurements determined multiple times to determine thevariation profiles across multiple presentations. Determining variousprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. The synchronous response of multiple individuals to stimulipresented in concert is measured to determine an enhanced across subjectsynchrony measure of effectiveness. According to various examples, thesynchronous response may be determined for multiple subjects residing inseparate locations or for multiple subjects residing in the samelocation.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied—in sequence orin parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhancedsignificance data, additional cross-modality synthesis mechanisms canalso be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR,EOG, and facial emotion encoding are connected to a cross-modalitysynthesis mechanism. Other mechanisms as well as variations andenhancements on existing mechanisms may also be included. According tovarious examples, data from a specific modality can be enhanced usingdata from one or more other modalities. In particular examples, EEGtypically makes frequency measurements in different bands like alpha,beta and gamma to provide estimates of significance. However, thetechniques of the present disclosure recognize that significancemeasures can be enhanced further using information from othermodalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of significance including but not limited to attention,emotional engagement, and memory retention. According to variousexamples, a cross-modality synthesis mechanism performs time and phaseshifting of data to allow data from different modalities to align. Insome examples, it is recognized that an EEG response will often occurhundreds of milliseconds before a facial emotion measurement changes.Correlations can be drawn and time and phase shifts made on anindividual as well as a group basis. In other examples, saccadic eyemovements may be determined as occurring before and after particular EEGresponses. According to various examples, time corrected GSR measuresare used to scale and enhance the EEG estimates of significanceincluding attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. According to various examples, ERP measures are enhanced usingEEG time-frequency measures (ERPSP) in response to the presentation ofthe marketing and entertainment stimuli. Specific portions are extractedand isolated to identify ERP, DERP and ERPSP analyses to perform. Inparticular examples, an EEG frequency estimation of attention, emotionand memory retention (ERPSP) is used as a co-factor in enhancing theERP, DERP and time-domain response analysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. According to variousexamples, EOG and eye tracking is enhanced by measuring the presence oflambda waves (a neurophysiological index of saccade effectiveness) inthe ongoing EEG in the occipital and extra striate regions, triggered bythe slope of saccade-onset to estimate the significance of the EOG andeye tracking measures. In particular examples, specific EEG signaturesof activity such as slow potential shifts and measures of coherence intime-frequency responses at the Frontal Eye Field (FEF) regions thatpreceded saccade-onset are measured to enhance the effectiveness of thesaccadic activity data.

GSR typically measures the change in general arousal in response tostimulus presented. According to various examples, GSR is enhanced bycorrelating EEG/ERP responses and the GSR measurement to get an enhancedestimate of subject engagement. The GSR latency baselines are used inconstructing a time-corrected GSR response to the stimulus. Thetime-corrected GSR response is co-factored with the EEG measures toenhance GSR significance measures.

According to various examples, facial emotion encoding uses templatesgenerated by measuring facial muscle positions and movements ofindividuals expressing various emotions prior to the testing session.These individual specific facial emotion encoding templates are matchedwith the individual responses to identify subject emotional response. Inparticular examples, these facial emotion encoding measurements areenhanced by evaluating inter-hemispherical asymmetries in EEG responsesin specific frequency bands and measuring frequency band interactions.The techniques of the present disclosure recognize that not only areparticular frequency bands significant in EEG responses, but particularfrequency bands used for communication between particular areas of thebrain are significant. Consequently, these EEG responses enhance theEMG, graphic and video based facial emotion identification.

According to various examples, post-stimulus versus pre-stimulusdifferential measurements of ERP time domain components in multipleregions of the brain (DERP) are measured at 607. The differentialmeasures give a mechanism for eliciting responses attributable to thestimulus. For example the messaging response attributable to anadvertisement or the brand response attributable to multiple brands isdetermined using pre-resonance and post-resonance estimates

At 609, target versus distracter stimulus differential responses aredetermined for different regions of the brain (DERP). At 611, eventrelated time-frequency analysis of the differential response (DERPSPs)are used to assess the attention, emotion and memory retention measuresacross multiple frequency bands. According to various examples, themultiple frequency bands include theta, alpha, beta, gamma and highgamma or kappa. At 613, priming levels and resonance for variousproducts, services, and offerings are determined at different locationsin the stimulus material. In some examples, priming levels and resonanceare manually determined. In other examples, priming levels and resonanceare automatically determined using neuro-response measurements.According to various examples, video streams are modified with differentinserted advertisements for various products and services to determinethe effectiveness of the inserted advertisements based on priming levelsand resonance of the source material.

At 617, multiple trials are performed to enhance priming and resonancemeasures. In some examples, stimulus. In some examples, multiple trialsare performed to enhance resonance measures.

In particular examples, the priming and resonance measures are sent to apriming repository 619. The priming repository 619 may be used toautomatically select advertising suited for particular ad breaks.

FIG. 7 illustrates an example of a technique for estimating resonance.According to various examples, measurements from different modalitiesare obtained. According to various examples, measurements includingDifferential Event Related Potential (DERP), Differential Event RelatedPower Spectral Perturbations (DERPSPs), Pupilary Response, etc., areblended to obtain a combined measurement. In particular examples, eachmeasurement may have to be aligned appropriately in order to allowblending. According to various examples, a resonance estimator includesmechanisms to use and blend different measures from across themodalities from the data analyzer. In particular examples, the dataincludes the DERP measures, DERPSPs, pupilary response, GSR, eyemovement, coherence, coupling and lambda wave based response.Measurements across modalities are blended to elicit a synthesizedmeasure of user resonance.

In particular examples, user resonance to attributes of stimulusmaterial such as communication, concept, experience, message, images,genre, product categories, service categories, etc. are measured at 701.The attributes of the stimulus material are evaluated to identify adcategories and genres that are naturally primed as a consequence of thepreceding content 703. The effectiveness of source material may bedetermined using a mechanism to weigh and combine the outputs of thedata analyzer. According to various examples, a set of predeterminedweights and nonlinear functions combine the outputs of the data analyzerto determine a hierarchy of the effectiveness of a set of categories forproducts and services that are primed by the source material orpre-break show content at 705. According to various examples, a set ofpredetermined weights and nonlinear functions combine the outputs of thedata analyzer to determine scenes of a show of maximal effectiveness toperform a differential extraction of categories of products and servicesthat are effectively primed by the source material at 707.

At 711, priming levels for various products and services are correlatedwith various ad breaks based on the number of scenes of maximaleffectiveness, the number of categories of products and services primedby the pre break content, and the level of priming effectiveness foreach category.

At 713, priming levels and resonance are maintained in a priming levelrepository. In some examples, the priming levels and resonance arewritten to the source material.

According to various examples, various mechanisms such as the datacollection mechanisms, the intra-modality synthesis mechanisms,cross-modality synthesis mechanisms, etc. are implemented on multipledevices. However, it is also possible that the various mechanisms beimplemented in hardware, firmware, and/or software in a single system.FIG. 8 provides one example of a system that can be used to implementone or more mechanisms. For example, the system shown in FIG. 8 may beused to implement a resonance measurement system.

According to particular examples, a system 800 suitable for implementingparticular examples of the present disclosure includes a processor 801,a memory 803, an interface 811, and a bus 815 (e.g., a PCI bus). Whenacting under the control of appropriate software or firmware, theprocessor 801 is responsible for such tasks such as pattern generation.Various specially configured devices can also be used in place of aprocessor 801 or in addition to processor 801. The completeimplementation can also be done in custom hardware. The interface 811 istypically configured to send and receive data packets or data segmentsover a network. Particular examples of interfaces the device supportsinclude host bus adapter (HBA) interfaces, Ethernet interfaces, framerelay interfaces, cable interfaces, DSL interfaces, token ringinterfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as data synthesis.

According to particular examples, the system 800 uses memory 803 tostore data, algorithms and program instructions. The programinstructions may control the operation of an operating system and/or oneor more applications, for example. The memory or memories may also beconfigured to store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present disclosurerelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks and DVDs;magneto-optical media such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory devices (ROM) and random access memory (RAM).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Although the foregoing disclosure has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Therefore, the present examples are to be considered asillustrative and not restrictive and the disclosure is not to be limitedto the details given herein, but may be modified within the scope andequivalents of the appended claims.

What is claimed is:
 1. A system to identify an advertisement to includein source material to increase an effectiveness of the advertisement,the system comprising: an analyzer to determine one or more primingcharacteristics for a plurality of locations of a source material basedon neuro-response data collected from a first subject exposed to thesource material, the neuro-response data indicative of a resonance ofthe first subject to the plurality of locations; and a selector to:identify an attribute of the advertisement; identify at least one of atemporal attribute or a spatial attribute for the plurality oflocations; perform a comparison of the attribute of the advertisement tothe at least one of the temporal attribute or the spatial attribute forthe plurality of locations; select a first location of the plurality oflocations for insertion of the advertisement based on the comparison andthe priming characteristics; and transform the source material toinclude the advertisement at the first location.
 2. The system of claim1, wherein the selector is to identify the temporal attribute or thespatial attribute based on a meta-tag associated with the sourcematerial.
 3. The system of claim 1, wherein the attribute of theadvertisement includes a product category and wherein the selector is tofurther perform the comparison based on the product category and contentof the source material.
 4. The system of claim 1, wherein the selectoris to further perform the comparison based on the resonance of the firstsubject.
 5. The system of claim 1, wherein the analyzer is to tag thesource material with one or more of the resonance or the primingcharacteristics.
 6. The system of claim 1, wherein the analyzer is todetermine an effectiveness of the advertisement at the first locationbased on second neuro-response data collected from the first subject ora second subject exposed to the source material including theadvertisement at the first location and the selector is to select asecond location of the source material to receive the advertisement or asecond advertisement based on the effectiveness.
 7. The system of claim1, wherein the attribute of the advertisement includes a format of theadvertisement and the spatial attribute for the one or more locationsincludes a format of the source material and wherein the selector is tocorrelate to the format of the advertisement with the format of thesource material.
 8. A tangible machine readable storage device orstorage disc comprising instructions which, when executed by a machine,cause the machine to at least: determine one or more primingcharacteristics for a plurality of locations of a source material basedon neuro-response data collected from a first subject exposed to thesource material, the neuro-response data indicative of a resonance ofthe first subject to the plurality of locations; identify an attributeof the advertisement; identify at least one of a temporal attribute or aspatial attribute for the plurality of locations; perform a comparisonof the attribute of the advertisement to the at least one of thetemporal attribute or the spatial attribute for the plurality oflocations; select a first location of the plurality of locations forinsertion of the advertisement based on the comparison and the primingcharacteristics; and transform the source material to include theadvertisement at the first location.
 9. The storage device or storagedisk of claim 8, wherein the instructions further cause the machine toidentify the temporal attribute or the spatial attribute based on ameta-tag associated with the source material.
 10. The storage device orstorage disk of claim 8, wherein the attribute of the advertisementincludes a product category, and the instructions further cause themachine to perform the comparison based on the product category andcontent of the source material.
 11. The storage device or storage diskof claim 8, wherein the instructions further cause the machine toperform the comparison based on the resonance of the first subject. 12.The storage device or storage disk of claim 8, wherein the instructionsfurther cause the machine to tag the source material with one or more ofthe resonance or the priming characteristics.
 13. The storage device orstorage disk of claim 8, wherein the instructions further cause themachine to determine an effectiveness of the advertisement at the firstlocation based on second neuro-response data collected from the firstsubject or a second subject exposed to the source material including theadvertisement at the first location and select a second location of thesource material to receive the advertisement or a second advertisementbased on the effectiveness.
 14. The storage device or storage disk ofclaim 8, wherein the attribute of the advertisement includes a format ofthe advertisement and the spatial attribute for the plurality oflocations includes a format of the source material and wherein theinstructions further cause the machine to correlate to the format of theadvertisement with the format of the source material.
 15. A methodcomprising: determining, by executing an instruction with a processor,one or more priming characteristics for a plurality of locations of asource material based on neuro-response data collected from a firstsubject exposed to the source material, the neuro-response dataindicative of a resonance of the first subject to the plurality oflocations; identifying, by executing an instruction with the processor,an attribute of the advertisement; identifying, by executing aninstruction with the processor at least one of a temporal attribute or aspatial attribute for the plurality of locations; performing, byexecuting an instruction with the processor, a comparison of theattribute of the advertisement to the at least one of the temporalattribute or the spatial attribute for the plurality of locations;selecting, by executing an instruction with the processor, a firstlocation of the plurality of locations for insertion of theadvertisement based on the comparison and the priming characteristics;and transforming, by executing an instruction with the processor, thesource material to include the advertisement at the first location. 16.The method of claim 15, further including identifying the temporalattribute or the spatial attribute based on a meta-tag associated withthe source material.
 17. The method of claim 15, wherein the attributeof the advertisement includes a product category, and further includingperforming the comparison based on the product category and content ofthe source material.
 18. The method of claim 15, further includingperforming the comparison based on the resonance of the first subject.19. The method of claim 15, further including tagging the sourcematerial with one or more of the resonance or the primingcharacteristics.
 20. The method of claim 15, further includingdetermining an effectiveness of the advertisement at the first locationbased on second neuro-response data collected from the first subject ora second subject exposed to the source material including theadvertisement at the first location and selecting a second location ofthe source material to receive the advertisement or a secondadvertisement based on the effectiveness.